Do Conventional Monetary Policy Instruments Matter in Unconventional Times?
Manuel Buchholz, Kirsten Schmidt, Lena Tonzer
Abstract
This paper investigates how declines in the deposit facility rate set by the European Central Bank (ECB) affect bank behavior. The ECB aims to reduce banks’ incentives to hold reserves at the central bank and thus to encourage loan supply. However, given depressed margins in a low interest environment, banks might reallocate their liquidity toward more profitable liquid assets other than traditional loans. Our analysis is based on a sample of euro area banks for the period from 2009 to 2014. Three key findings arise. First, banks reduce their reserve holdings following declines in the deposit facility rate. Second, this effect is heterogeneous across banks depending on their business model. Banks with a more interest-sensitive business model are more responsive to changes in the deposit facility rate. Third, there is evidence of a reallocation of liquidity toward loans but not toward other liquid assets. This result is most pronounced for non-GIIPS countries of the euro area.
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Tail-risk Protection Trading Strategies
Natalie Packham, Jochen Papenbrock, Peter Schwendner, Fabian Wöbbeking
Quantitative Finance,
No. 5,
2017
Abstract
Starting from well-known empirical stylized facts of financial time series, we develop dynamic portfolio protection trading strategies based on econometric methods. As a criterion for riskiness, we consider the evolution of the value-at-risk spread from a GARCH model with normal innovations relative to a GARCH model with generalized innovations. These generalized innovations may for example follow a Student t, a generalized hyperbolic, an alpha-stable or a Generalized Pareto distribution (GPD). Our results indicate that the GPD distribution provides the strongest signals for avoiding tail risks. This is not surprising as the GPD distribution arises as a limit of tail behaviour in extreme value theory and therefore is especially suited to deal with tail risks. Out-of-sample backtests on 11 years of DAX futures data, indicate that the dynamic tail-risk protection strategy effectively reduces the tail risk while outperforming traditional portfolio protection strategies. The results are further validated by calculating the statistical significance of the results obtained using bootstrap methods. A number of robustness tests including application to other assets further underline the effectiveness of the strategy. Finally, by empirically testing for second-order stochastic dominance, we find that risk averse investors would be willing to pay a positive premium to move from a static buy-and-hold investment in the DAX future to the tail-risk protection strategy.
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Complex-task Biased Technological Change and the Labor Market
Colin Caines, Florian Hoffmann, Gueorgui Kambourov
Review of Economic Dynamics,
April
2017
Abstract
In this paper we study the relationship between task complexity and the occupational wage- and employment structure. Complex tasks are defined as those requiring higher-order skills, such as the ability to abstract, solve problems, make decisions, or communicate effectively. We measure the task complexity of an occupation by performing Principal Component Analysis on a broad set of occupational descriptors in the Occupational Information Network (O*NET) data. We establish four main empirical facts for the U.S. over the 1980–2005 time period that are robust to the inclusion of a detailed set of controls, subsamples, and levels of aggregation: (1) There is a positive relationship across occupations between task complexity and wages and wage growth; (2) Conditional on task complexity, routine-intensity of an occupation is not a significant predictor of wage growth and wage levels; (3) Labor has reallocated from less complex to more complex occupations over time; (4) Within groups of occupations with similar task complexity labor has reallocated to non-routine occupations over time. We then formulate a model of Complex-Task Biased Technological Change with heterogeneous skills and show analytically that it can rationalize these facts. We conclude that workers in non-routine occupations with low ability of solving complex tasks are not shielded from the labor market effects of automatization.
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Financial Transaction Taxes: Announcement Effects, Short-run Effects, and Long-run Effects
Sebastian Eichfelder, Mona Noack, Felix Noth
Abstract
We analyze the impact of the French 2012 financial transaction tax (FTT) on trading volumes, stock prices, liquidity, and volatility. We extend the empirical research by identifying FTT announcement and short-run treatment effects, which can distort difference-in-differences estimates. In addition, we consider long-run volatility measures that better fit the French FTT’s legislative design. While we find strong evidence of a positive FTT announcement effect on trading volumes, there is almost no statistically significant evidence of a long-run treatment effect. Thus, evidence of a strong reduction of trading volumes resulting from the French FTT might be driven by announcement effects and short-term treatment effects. We find evidence of an increase of intraday volatilities in the announcement period and a significant reduction of weekly and monthly volatilities in the treatment period. Our findings support theoretical considerations suggesting a stabilizing impact of FTTs on financial markets.
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Creative Destruction and Subjective Well-being
Philippe Aghion, Ufuk Akcigit, Angus Deaton, Alexandra Roulet
American Economic Review,
No. 12,
2016
Abstract
In this paper we analyze the relationship between turnover-driven growth and subjective well-being. Our model of innovation-led growth and unemployment predicts that: (i) the effect of creative destruction on expected individual welfare should be unambiguously positive if we control for unemployment, less so if we do not; (ii) job creation has a positive and job destruction has a negative impact on well-being; (iii) job destruction has a less negative impact in areas with more generous unemployment insurance policies; and (iv) job creation has a more positive effect on individuals that are more forward-looking. The empirical analysis using cross sectional MSA (metropolitan statistical area)-level and individual-level data provide empirical support to these predictions.
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The European Refugee Crisis and the Natural Rate of Output
Katja Heinisch, Klaus Wohlrabe
Abstract
The European Commission follows a harmonized approach for calculating structural (potential) output for EU member states that takes into account labor as an important ingredient. This paper shows how the recent huge migrants inflow to Europe affects trend output. Due to the fact that the immigrants immediately increase the working population but effectively do not enter the labor market, we illustrate that the potential output is potentially upward biased without any corrections. Taking Germany as an example, we find that the average medium-term potential growth rate is lower if the migration flow is modeled adequately compared to results based on the unadjusted European Commission procedure.
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The Macroeconomic Risks of Undesirably Low Inflation
Jonas Arias, Christopher J. Erceg, Mathias Trabandt
European Economic Review,
2016
Abstract
This paper investigates the macroeconomic risks associated with undesirably low inflation using a medium-sized New Keynesian model. We consider different causes of persistently low inflation, including a downward shift in long-run inflation expectations, a fall in nominal wage growth, and a favorable supply-side shock. We show that the macroeconomic effects of persistently low inflation depend crucially on its underlying cause, as well as on the extent to which monetary policy is constrained by the zero lower bound. Finally, we discuss policy options to mitigate these effects.
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Impulse Response Analysis in a Misspecified DSGE Model: A Comparison of Full and Limited Information Techniques
Sebastian Giesen, Rolf Scheufele
Applied Economics Letters,
No. 3,
2016
Abstract
In this article, we examine the effect of estimation biases – introduced by model misspecification – on the impulse responses analysis for dynamic stochastic general equilibrium (DSGE) models. Thereby, we use full and limited information estimators to estimate a misspecified DSGE model and calculate impulse response functions (IRFs) based on the estimated structural parameters. It turns out that IRFs based on full information techniques can be unreliable under misspecification.
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Lend Global, Fund Local? Price and Funding Cost Margins in Multinational Banking
Rients Galema, Michael Koetter, C. Liesegang
Review of Finance,
No. 5,
2016
Abstract
In a proposed model of a multinational bank, interest margins determine local lending by foreign affiliates and the internal funding by parent banks. We exploit detailed parent-affiliate-level data of all German banks to empirically test our theoretical predictions in pre-crisis times. Local lending by affiliates depends negatively on price margins, the difference between lending and deposit rates in foreign markets. The effect of funding cost margins, the gap between local deposit rates faced by affiliates abroad and the funding costs of their parents, on internal capital market funding is positive but statistically weak. Interest margins are central to explain the interaction between internal capital markets and foreign affiliates lending.
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Effects of Incorrect Specification on the Finite Sample Properties of Full and Limited Information Estimators in DSGE Models
Sebastian Giesen, Rolf Scheufele
Journal of Macroeconomics,
June
2016
Abstract
In this paper we analyze the small sample properties of full information and limited information estimators in a potentially misspecified DSGE model. Therefore, we conduct a simulation study based on a standard New Keynesian model including price and wage rigidities. We then study the effects of omitted variable problems on the structural parameter estimates of the model. We find that FIML performs superior when the model is correctly specified. In cases where some of the model characteristics are omitted, the performance of FIML is highly unreliable, whereas GMM estimates remain approximately unbiased and significance tests are mostly reliable.
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